Overview

Dataset statistics

Number of variables13
Number of observations789
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory80.3 KiB
Average record size in memory104.2 B

Variable types

Categorical2
Numeric11

Alerts

id has a high cardinality: 787 distinct values High cardinality
num_nodes is highly correlated with num_tweets and 5 other fieldsHigh correlation
num_tweets is highly correlated with num_nodes and 4 other fieldsHigh correlation
avg_num_retweet is highly correlated with num_nodes and 5 other fieldsHigh correlation
retweet_perc is highly correlated with avg_num_retweetHigh correlation
num_users is highly correlated with num_nodes and 5 other fieldsHigh correlation
total_propagation_time is highly correlated with num_nodes and 5 other fieldsHigh correlation
avg_num_followers is highly correlated with avg_num_retweetHigh correlation
avg_time_diff is highly correlated with num_nodes and 5 other fieldsHigh correlation
users_10h is highly correlated with num_nodes and 4 other fieldsHigh correlation
num_nodes is highly correlated with num_tweets and 1 other fieldsHigh correlation
num_tweets is highly correlated with num_nodes and 2 other fieldsHigh correlation
avg_num_retweet is highly correlated with retweet_percHigh correlation
retweet_perc is highly correlated with avg_num_retweetHigh correlation
num_users is highly correlated with num_nodes and 1 other fieldsHigh correlation
users_10h is highly correlated with num_tweetsHigh correlation
num_nodes is highly correlated with num_tweets and 4 other fieldsHigh correlation
num_tweets is highly correlated with num_nodes and 3 other fieldsHigh correlation
avg_num_retweet is highly correlated with retweet_percHigh correlation
retweet_perc is highly correlated with avg_num_retweetHigh correlation
num_users is highly correlated with num_nodes and 4 other fieldsHigh correlation
total_propagation_time is highly correlated with num_nodes and 1 other fieldsHigh correlation
avg_time_diff is highly correlated with num_nodes and 2 other fieldsHigh correlation
users_10h is highly correlated with num_nodes and 2 other fieldsHigh correlation
label is highly correlated with total_propagation_timeHigh correlation
num_nodes is highly correlated with num_tweets and 1 other fieldsHigh correlation
num_tweets is highly correlated with num_nodes and 2 other fieldsHigh correlation
avg_num_retweet is highly correlated with retweet_percHigh correlation
retweet_perc is highly correlated with avg_num_retweetHigh correlation
num_users is highly correlated with num_nodes and 2 other fieldsHigh correlation
total_propagation_time is highly correlated with label and 1 other fieldsHigh correlation
avg_num_followers is highly correlated with avg_num_friendsHigh correlation
avg_num_friends is highly correlated with avg_num_followersHigh correlation
perc_post_1_hour is highly correlated with total_propagation_timeHigh correlation
users_10h is highly correlated with num_tweets and 1 other fieldsHigh correlation
avg_num_followers is highly skewed (γ1 = 20.9278475) Skewed
avg_time_diff is highly skewed (γ1 = 25.6748825) Skewed
id is uniformly distributed Uniform
avg_num_retweet has 147 (18.6%) zeros Zeros
avg_time_diff has 147 (18.6%) zeros Zeros

Reproduction

Analysis started2021-11-08 10:28:24.128919
Analysis finished2021-11-08 10:29:03.476649
Duration39.35 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

label
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
real
404 
fake
385 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfake
2nd rowfake
3rd rowfake
4th rowfake
5th rowfake

Common Values

ValueCountFrequency (%)
real404
51.2%
fake385
48.8%

Length

2021-11-08T15:59:03.574282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-08T15:59:03.691920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
real404
51.2%
fake385
48.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

num_nodes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct441
Distinct (%)55.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1332.382763
Minimum2
Maximum53494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2021-11-08T15:59:03.789732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.4
Q117
median106
Q3646
95-th percentile5084.2
Maximum53494
Range53492
Interquartile range (IQR)629

Descriptive statistics

Standard deviation4850.472585
Coefficient of variation (CV)3.640449816
Kurtosis60.22852804
Mean1332.382763
Median Absolute Deviation (MAD)102
Skewness7.098983769
Sum1051250
Variance23527084.3
MonotonicityNot monotonic
2021-11-08T15:59:03.981175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240
 
5.1%
325
 
3.2%
421
 
2.7%
617
 
2.2%
514
 
1.8%
710
 
1.3%
1010
 
1.3%
119
 
1.1%
99
 
1.1%
168
 
1.0%
Other values (431)626
79.3%
ValueCountFrequency (%)
240
5.1%
325
3.2%
421
2.7%
514
 
1.8%
617
2.2%
710
 
1.3%
88
 
1.0%
99
 
1.1%
1010
 
1.3%
119
 
1.1%
ValueCountFrequency (%)
534941
0.1%
533511
0.1%
518301
0.1%
374001
0.1%
353791
0.1%
303441
0.1%
287451
0.1%
279491
0.1%
247131
0.1%
210391
0.1%

num_tweets
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct372
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean538.7097592
Minimum1
Maximum21831
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2021-11-08T15:59:04.160116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q111
median67
Q3358
95-th percentile2038.8
Maximum21831
Range21830
Interquartile range (IQR)347

Descriptive statistics

Standard deviation1739.514509
Coefficient of variation (CV)3.229038419
Kurtosis67.51485632
Mean538.7097592
Median Absolute Deviation (MAD)64
Skewness7.335307488
Sum425042
Variance3025910.727
MonotonicityNot monotonic
2021-11-08T15:59:04.331086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149
 
6.2%
233
 
4.2%
422
 
2.8%
518
 
2.3%
315
 
1.9%
814
 
1.8%
913
 
1.6%
1012
 
1.5%
1111
 
1.4%
1910
 
1.3%
Other values (362)592
75.0%
ValueCountFrequency (%)
149
6.2%
233
4.2%
315
 
1.9%
422
2.8%
518
 
2.3%
69
 
1.1%
78
 
1.0%
814
 
1.8%
913
 
1.6%
1012
 
1.5%
ValueCountFrequency (%)
218311
0.1%
206961
0.1%
145181
0.1%
136121
0.1%
123161
0.1%
107191
0.1%
104911
0.1%
98231
0.1%
88381
0.1%
87341
0.1%

avg_num_retweet
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct553
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9363495261
Minimum0
Maximum19
Zeros147
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2021-11-08T15:59:04.494837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09278350515
median0.4179566563
Q31.14747191
95-th percentile3.238540323
Maximum19
Range19
Interquartile range (IQR)1.054688405

Descriptive statistics

Standard deviation1.661498809
Coefficient of variation (CV)1.774442943
Kurtosis40.32133563
Mean0.9363495261
Median Absolute Deviation (MAD)0.4179566563
Skewness5.292222977
Sum738.7797761
Variance2.760578292
MonotonicityNot monotonic
2021-11-08T15:59:04.681201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0147
 
18.6%
0.514
 
1.8%
0.259
 
1.1%
19
 
1.1%
0.28
 
1.0%
0.33333333335
 
0.6%
0.14285714294
 
0.5%
0.44
 
0.5%
0.11764705883
 
0.4%
0.16666666673
 
0.4%
Other values (543)583
73.9%
ValueCountFrequency (%)
0147
18.6%
0.0068493150681
 
0.1%
0.010909090911
 
0.1%
0.013157894741
 
0.1%
0.019230769231
 
0.1%
0.022988505751
 
0.1%
0.023724792411
 
0.1%
0.028571428571
 
0.1%
0.029126213591
 
0.1%
0.029411764712
 
0.3%
ValueCountFrequency (%)
191
0.1%
16.61
0.1%
141
0.1%
12.611111111
0.1%
11.694444441
0.1%
11.307692311
0.1%
9.51
0.1%
81
0.1%
7.251
0.1%
72
0.3%

retweet_perc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct573
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3835610884
Minimum0.008
Maximum0.950310559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2021-11-08T15:59:04.980405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.05902293121
Q10.1954137587
median0.3532526475
Q30.5452926775
95-th percentile0.7641368061
Maximum0.950310559
Range0.942310559
Interquartile range (IQR)0.3498789188

Descriptive statistics

Standard deviation0.2243638211
Coefficient of variation (CV)0.5849493807
Kurtosis-0.8365782799
Mean0.3835610884
Median Absolute Deviation (MAD)0.1746812189
Skewness0.3185999007
Sum302.6296987
Variance0.05033912422
MonotonicityNot monotonic
2021-11-08T15:59:05.150257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.549
 
6.2%
0.333333333328
 
3.5%
0.2519
 
2.4%
0.166666666716
 
2.0%
0.216
 
2.0%
0.666666666710
 
1.3%
0.14285714299
 
1.1%
0.1258
 
1.0%
0.11111111117
 
0.9%
0.16
 
0.8%
Other values (563)621
78.7%
ValueCountFrequency (%)
0.0081
0.1%
0.0097087378641
0.1%
0.011494252871
0.1%
0.013513513511
0.1%
0.014285714291
0.1%
0.014336917561
0.1%
0.019607843141
0.1%
0.021739130431
0.1%
0.024305555561
0.1%
0.025641025641
0.1%
ValueCountFrequency (%)
0.9503105591
0.1%
0.94382022471
0.1%
0.93751
0.1%
0.92663043481
0.1%
0.92126845271
0.1%
0.91925465841
0.1%
0.90909090911
0.1%
0.91
0.1%
0.88888888891
0.1%
0.88235294122
0.3%

num_users
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct423
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1106.467681
Minimum1
Maximum48066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2021-11-08T15:59:05.315742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q115
median96
Q3569
95-th percentile4526.2
Maximum48066
Range48065
Interquartile range (IQR)554

Descriptive statistics

Standard deviation3940.293362
Coefficient of variation (CV)3.561146368
Kurtosis65.36642747
Mean1106.467681
Median Absolute Deviation (MAD)93
Skewness7.28156878
Sum873003
Variance15525911.78
MonotonicityNot monotonic
2021-11-08T15:59:05.478805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143
 
5.4%
229
 
3.7%
321
 
2.7%
515
 
1.9%
914
 
1.8%
613
 
1.6%
413
 
1.6%
812
 
1.5%
159
 
1.1%
128
 
1.0%
Other values (413)612
77.6%
ValueCountFrequency (%)
143
5.4%
229
3.7%
321
2.7%
413
 
1.6%
515
 
1.9%
613
 
1.6%
76
 
0.8%
812
 
1.5%
914
 
1.8%
104
 
0.5%
ValueCountFrequency (%)
480661
0.1%
443911
0.1%
401671
0.1%
281921
0.1%
281271
0.1%
231831
0.1%
218811
0.1%
204441
0.1%
194351
0.1%
168381
0.1%

total_propagation_time
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct769
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1484083033
Minimum1194543862
Maximum1545067305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2021-11-08T15:59:05.643080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1194543862
5-th percentile1265870943
Q11489767463
median1517397109
Q31535417754
95-th percentile1544739306
Maximum1545067305
Range350523443
Interquartile range (IQR)45650291

Descriptive statistics

Standard deviation84973658.63
Coefficient of variation (CV)0.05725667416
Kurtosis2.451961535
Mean1484083033
Median Absolute Deviation (MAD)22118498
Skewness-1.888460223
Sum1.170941513 × 1012
Variance7.22052266 × 1015
MonotonicityNot monotonic
2021-11-08T15:59:05.800946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15390870844
 
0.5%
15450511103
 
0.4%
12212183442
 
0.3%
15342472352
 
0.3%
15336649342
 
0.3%
13123777022
 
0.3%
15438248602
 
0.3%
15449955582
 
0.3%
13813193422
 
0.3%
15440428732
 
0.3%
Other values (759)766
97.1%
ValueCountFrequency (%)
11945438621
0.1%
11968510241
0.1%
11999292521
0.1%
12045048371
0.1%
12106870971
0.1%
12155980541
0.1%
12178593261
0.1%
12183773591
0.1%
12193418651
0.1%
12195447251
0.1%
ValueCountFrequency (%)
15450673051
 
0.1%
15450616221
 
0.1%
15450511103
0.4%
15450006821
 
0.1%
15449968571
 
0.1%
15449956941
 
0.1%
15449955582
0.3%
15449877771
 
0.1%
15449695881
 
0.1%
15449667751
 
0.1%

avg_num_followers
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct783
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29596.19598
Minimum0
Maximum4619920.091
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2021-11-08T15:59:06.000456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile461.4
Q12549
median6183.75
Q318280.88745
95-th percentile95080.24761
Maximum4619920.091
Range4619920.091
Interquartile range (IQR)15731.88745

Descriptive statistics

Standard deviation184480.8795
Coefficient of variation (CV)6.233263208
Kurtosis497.2941772
Mean29596.19598
Median Absolute Deviation (MAD)4594.715517
Skewness20.9278475
Sum23351398.63
Variance3.403319491 × 1010
MonotonicityNot monotonic
2021-11-08T15:59:06.169301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11912
 
0.3%
402
 
0.3%
2211.42
 
0.3%
702
 
0.3%
1210.752
 
0.3%
4747.1142862
 
0.3%
44573.941181
 
0.1%
5027.4098841
 
0.1%
7227.61
 
0.1%
41
 
0.1%
Other values (773)773
98.0%
ValueCountFrequency (%)
01
0.1%
41
0.1%
61
0.1%
91
0.1%
111
0.1%
121
0.1%
151
0.1%
17.51
0.1%
402
0.3%
461
0.1%
ValueCountFrequency (%)
4619920.0911
0.1%
15239421
0.1%
1441710.3751
0.1%
683587.54971
0.1%
414038.95741
0.1%
238770.54551
0.1%
220332.56291
0.1%
211301.25561
0.1%
207834.00931
0.1%
203024.95351
0.1%

avg_num_friends
Real number (ℝ≥0)

HIGH CORRELATION

Distinct778
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3040.327051
Minimum0
Maximum47800
Zeros5
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2021-11-08T15:59:06.344858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile348.3
Q11641.142857
median2492.357088
Q33496.014368
95-th percentile6324.69063
Maximum47800
Range47800
Interquartile range (IQR)1854.871511

Descriptive statistics

Standard deviation3402.959364
Coefficient of variation (CV)1.119274113
Kurtosis72.12175078
Mean3040.327051
Median Absolute Deviation (MAD)925.9095785
Skewness7.136923169
Sum2398818.043
Variance11580132.43
MonotonicityNot monotonic
2021-11-08T15:59:06.529865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05
 
0.6%
2067.3333332
 
0.3%
837.52
 
0.3%
2423.0222222
 
0.3%
592.52
 
0.3%
1620.752
 
0.3%
1082
 
0.3%
1517.8571432
 
0.3%
1330.5047621
 
0.1%
2024.4476741
 
0.1%
Other values (768)768
97.3%
ValueCountFrequency (%)
05
0.6%
0.51
 
0.1%
11
 
0.1%
71
 
0.1%
121
 
0.1%
161
 
0.1%
181
 
0.1%
19.51
 
0.1%
21.637681161
 
0.1%
291
 
0.1%
ValueCountFrequency (%)
478001
0.1%
39428.21
0.1%
362481
0.1%
28549.51
0.1%
21768.81
0.1%
21611.64391
0.1%
18924.097561
0.1%
16817.8751
0.1%
158091
0.1%
15786.810811
0.1%

avg_time_diff
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct635
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201014.9853
Minimum0
Maximum63001860
Zeros147
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2021-11-08T15:59:06.694172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1952.3333333
median18433.85606
Q356366.7474
95-th percentile406830.5353
Maximum63001860
Range63001860
Interquartile range (IQR)55414.41407

Descriptive statistics

Standard deviation2311829.228
Coefficient of variation (CV)11.50078052
Kurtosis693.9250338
Mean201014.9853
Median Absolute Deviation (MAD)18433.85606
Skewness25.6748825
Sum158600823.4
Variance5.344554379 × 1012
MonotonicityNot monotonic
2021-11-08T15:59:06.860249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0147
 
18.6%
1643
 
0.4%
141023.91672
 
0.3%
660.52
 
0.3%
45411.278872
 
0.3%
469469.96192
 
0.3%
2122
 
0.3%
32682
 
0.3%
2992214.6351
 
0.1%
3359.1666671
 
0.1%
Other values (625)625
79.2%
ValueCountFrequency (%)
0147
18.6%
241
 
0.1%
301
 
0.1%
681
 
0.1%
74.751
 
0.1%
851
 
0.1%
941
 
0.1%
107.51
 
0.1%
1081
 
0.1%
108.51
 
0.1%
ValueCountFrequency (%)
630018601
0.1%
9938771.5611
0.1%
7143712.51
0.1%
6593580.5561
0.1%
3690637.8611
0.1%
3275925.2811
0.1%
2992214.6351
0.1%
2822270.8771
0.1%
2428317.2291
0.1%
2098433.2431
0.1%

perc_post_1_hour
Real number (ℝ≥0)

HIGH CORRELATION

Distinct575
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4247681148
Minimum7.717538105 × 10-5
Maximum0.9994655265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2021-11-08T15:59:07.034888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7.717538105 × 10-5
5-th percentile0.004061802859
Q10.05555555556
median0.3333333333
Q30.8
95-th percentile0.9953074793
Maximum0.9994655265
Range0.9993883511
Interquartile range (IQR)0.7444444444

Descriptive statistics

Standard deviation0.3698604356
Coefficient of variation (CV)0.870734932
Kurtosis-1.424539745
Mean0.4247681148
Median Absolute Deviation (MAD)0.3076923077
Skewness0.3470508877
Sum335.1420426
Variance0.1367967418
MonotonicityNot monotonic
2021-11-08T15:59:07.205579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.555
 
7.0%
0.333333333325
 
3.2%
0.666666666713
 
1.6%
0.2513
 
1.6%
0.210
 
1.3%
0.16666666678
 
1.0%
0.14285714296
 
0.8%
0.090909090915
 
0.6%
0.1255
 
0.6%
0.076923076925
 
0.6%
Other values (565)644
81.6%
ValueCountFrequency (%)
7.717538105 × 10-51
0.1%
9.05715062 × 10-51
0.1%
0.00018878610531
0.1%
0.00020232266421
0.1%
0.0005260137721
0.1%
0.0005461496451
0.1%
0.00059844404551
0.1%
0.00067086386621
0.1%
0.00080128205131
0.1%
0.00082472680921
0.1%
ValueCountFrequency (%)
0.99946552651
0.1%
0.99935442221
0.1%
0.9992295841
0.1%
0.99917898191
0.1%
0.99912549191
0.1%
0.9991063451
0.1%
0.99904942971
0.1%
0.9989604991
0.1%
0.99892125131
0.1%
0.998899891
0.1%

users_10h
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct281
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.6539924
Minimum1
Maximum3987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2021-11-08T15:59:07.479449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median18
Q3170
95-th percentile884.2
Maximum3987
Range3986
Interquartile range (IQR)167

Descriptive statistics

Standard deviation338.9986534
Coefficient of variation (CV)2.034146608
Kurtosis37.18745223
Mean166.6539924
Median Absolute Deviation (MAD)17
Skewness4.577306066
Sum131490
Variance114920.087
MonotonicityNot monotonic
2021-11-08T15:59:07.660151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1136
 
17.2%
254
 
6.8%
333
 
4.2%
431
 
3.9%
618
 
2.3%
517
 
2.2%
816
 
2.0%
714
 
1.8%
913
 
1.6%
1013
 
1.6%
Other values (271)444
56.3%
ValueCountFrequency (%)
1136
17.2%
254
 
6.8%
333
 
4.2%
431
 
3.9%
517
 
2.2%
618
 
2.3%
714
 
1.8%
816
 
2.0%
913
 
1.6%
1013
 
1.6%
ValueCountFrequency (%)
39871
0.1%
37501
0.1%
15051
0.1%
15041
0.1%
14961
0.1%
13551
0.1%
13262
0.3%
12441
0.1%
12071
0.1%
11681
0.1%

id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct787
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size6.3 KiB
politifact14920
 
2
politifact14940
 
2
politifact11773
 
1
politifact15645
 
1
politifact1467
 
1
Other values (782)
782 

Length

Max length15
Median length15
Mean length14.41825095
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique785 ?
Unique (%)99.5%

Sample

1st rowpolitifact11773
2nd rowpolitifact13038
3rd rowpolitifact13467
4th rowpolitifact13468
5th rowpolitifact13475

Common Values

ValueCountFrequency (%)
politifact149202
 
0.3%
politifact149402
 
0.3%
politifact117731
 
0.1%
politifact156451
 
0.1%
politifact14671
 
0.1%
politifact149841
 
0.1%
politifact1501
 
0.1%
politifact15001
 
0.1%
politifact151331
 
0.1%
politifact15191
 
0.1%
Other values (777)777
98.5%

Length

2021-11-08T15:59:07.826710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
politifact149202
 
0.3%
politifact149402
 
0.3%
politifact150521
 
0.1%
politifact138151
 
0.1%
politifact136631
 
0.1%
politifact136171
 
0.1%
politifact135601
 
0.1%
politifact134671
 
0.1%
politifact134681
 
0.1%
politifact134751
 
0.1%
Other values (777)777
98.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-11-08T15:59:01.084745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:45.003433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:46.730151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:48.420359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:50.038703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:51.597413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:53.090520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:54.810822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:56.297996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:57.799909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:59.511002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:01.233589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:45.389722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:46.868142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:48.565922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:50.165848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:51.727151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:53.237512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:54.944114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:56.430995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:58.049948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:59.643834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:01.378447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:45.517814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:46.990372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:48.699049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:50.294864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:51.860018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:53.370468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:55.071001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:56.558316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:58.179306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:59.772006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:01.650329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:45.657997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:47.126116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:48.848108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:50.428744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:51.997352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:53.530240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:55.212767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:56.699976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:58.320731image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:59.913802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:01.806672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:45.786101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:47.249394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:48.989578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:50.555081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:52.122086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:53.667862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:55.343879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:56.832966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:58.452851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:00.052826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:01.966259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:45.915399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:47.380376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:49.137176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:50.680157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:52.255004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:53.811640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:55.481507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:56.960976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:58.590904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:00.202175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:02.128390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:46.059110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:47.702149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:49.340717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:50.822759image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:52.402653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:53.967124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:55.628191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:57.108459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:58.749345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:00.358588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:02.283755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:46.185101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:47.832158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:49.475650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:50.952930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:52.537602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:54.109007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:55.755541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:57.239399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:58.893923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:00.503333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:02.432298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:46.315949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:47.969186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:49.614259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:51.194483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:52.672926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:54.251709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:55.891509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:57.374560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:59.026860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:00.641148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:02.591766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:46.454204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:48.111667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:49.755241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:51.326103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:52.813561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:54.402466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:56.025333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:57.519340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:59.170385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:00.778764image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:02.752081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:46.586075image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:48.270319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:49.892185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:51.456545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:52.948490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:54.660203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:56.156401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:57.655319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:58:59.350704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-08T15:59:00.918932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-08T15:59:07.966769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-08T15:59:08.208064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-08T15:59:08.438694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-08T15:59:08.659676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-08T15:59:03.062024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-08T15:59:03.339012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

labelnum_nodesnum_tweetsavg_num_retweetretweet_percnum_userstotal_propagation_timeavg_num_followersavg_num_friendsavg_time_diffperc_post_1_hourusers_10hid
0fake124820.5000000.3387101221.454356e+096980.2032525410.72357766045.6311510.15322688politifact11773
1fake1290.2222220.250000111.486939e+092670.4545451903.00000028906.5000000.0833331politifact13038
2fake59400.4500000.322034471.543481e+093597.689655871.87931041604.9000000.61016923politifact13467
3fake3332190.5159820.3423423161.524245e+09109006.9668672361.521084160908.6896760.453453207politifact13468
4fake15307121.1474720.53464114211.506620e+093942.9156313699.54218490408.4235910.0013073politifact13475
5fake8825840.5085620.3378688541.494678e+0912791.2531212478.77185035012.8899540.218821574politifact13496
6fake28230.1739130.178571261.480380e+091225.5925931586.70370412304.3333330.42857119politifact13501
7fake29069312.1203010.67962823311.544116e+0911901.4870913328.49948457791.4749430.662423643politifact13515
8fake5332621.0305340.5084434961.481783e+093818.4981203862.22932316331.2031570.026266236politifact13544
9fake320.0000000.33333321.468308e+095077.5000005514.0000000.0000000.3333331politifact13557

Last rows

labelnum_nodesnum_tweetsavg_num_retweetretweet_percnum_userstotal_propagation_timeavg_num_followersavg_num_friendsavg_time_diffperc_post_1_hourusers_10hid
779real679819952.4070180.70653162971.544845e+099175.7696042731.30984361589.5447950.684466988politifact968
780real59470.2340430.203390521.412157e+0931973.17241413116.5862072557.3015870.79661041politifact9691
781real540.0000000.20000041.343229e+091121.250000511.2500000.0000000.2000001politifact975
782real1371300.0461540.0510951161.415111e+0919001.1617653143.7794123005.5555560.985401109politifact976
783real4472680.6641790.4004473711.529078e+09211301.2556051868.37892413801.2527460.988814219politifact979
784real5791443.0138890.7512955301.539831e+09105249.0328721702.24394546025.4651120.768566106politifact98
785real2811630.7177910.4199292021.508847e+0911056.6964293144.55714318515.5618870.12099647politifact9802
786real870.0000000.12500071.221920e+09119306.7142861641.1428570.0000000.8750007politifact986
787real580418622.1165410.67918752611.544043e+0970813.8199212289.45390354490.6235180.4658861008politifact99
788real70690.0000000.01428631.496078e+096697.69565221.6376810.0000000.0142861politifact997